The role of exploration and critical decision making and problem solving in making life choices

Although the literature on the types of abilities and processes that contribute to identity formation has been growing, the research has been mainly descriptive/correlational. This dissertation conducted an experimental investigation of the role of two theoretically distinct processes (exploration and critical problem solving) in identity formation, one of the first to be reported. The experimental training design (pre-post, training versus control) used in this study was intended to promote identity development by fostering an increase in the use of exploration and critical problem solving with respect to making life choices. Participants included 53 psychology students from a large urban university randomly assigned to each group. The most theoretically significant finding was that the intervention was successful in inducing change in the ability to use critical skills in resolving life decisions, as well as effecting a positive change in identity status. ^

The role of exploration and critical decision making and problem solving in making life choices

Although the literature on the types of abilities and processes that contribute to identity formation has been growing, the research has been mainly descriptive/correlational. This dissertation conducted an experimental investigation of the role of two theoretically distinct processes (exploration and critical problem solving) in identity formation, one of the first to be reported. The experimental training design (pre-post, training versus control) used in this study was intended to promote identity development by fostering an increase in the use of exploration and critical problem solving with respect to making life choices. Participants included 53 psychology students from a large urban university randomly assigned to each group. The most theoretically significant finding was that the intervention was successful in inducing change in the ability to use critical skills in resolving life decisions, as well as effecting a positive change in identity status.

The integrated use of simulation and PRS voting systems

Peer reviewed

The integrated use of simulation and PRS voting systems

Peer reviewed

Estudo empírico de antecedentes do sucesso de sistemas de informação: análise de um sistema de apoio à decisão em uma universidade pública

Universities are institutions that generate and manipulate large amounts of data as a result of the multiple functions they perform, of the amount of involved professionals and students they attend. Information gathered from these data is used, for example, for operational activities and to support decision-making by managers. To assist managers in accomplishing their tasks, the Information Systems (IS) are presented as tools that offer features aiming to improve the performance of its users, assist with routine tasks and provide support to decision-making. The purpose of this research is to evaluate the influence of the users features and of the task in the success of IS. The study is of a descriptive-exploratory nature, therefore, the constructs used to define the conceptual model of the research are known and previously validated. However, individual features of users and of the task are IS success antecedents. In order to test the influence of these antecedents, it was developed a decision support IS that uses the Multicriteria Decision Aid Constructivist (MCDA-C) methodology with the participation and involvement of users. The sample consisted of managers and former managers of UTFPR Campus Pato Branco who work or have worked in teaching activities, research, extension and management. For data collection an experiment was conducted in the computer lab of the Campus Pato Branco in order to verify the hypotheses of the research. The experiment consisted of performing a distribution task of teaching positions between the academic departments using the IS developed. The task involved decision-making related to management activities. The data that fed the system used were real, from the Campus itself. A questionnaire was answered by the participants of the experiment in order to obtain data to verify the research hypotheses. The results obtained from the data analysis partially confirmed the influence of the individual features in IS success and fully confirmed the influence of task features. The data collected failed to support significant ratio between the individual features and the individual impact. For many of the participants the first contact with the IS was during the experiment, which indicates the lack of experience with the system. Regarding the success of IS, the data revealed that there is no significance in the relationship between Information Quality (IQ) and Individual Impact (II). It is noteworthy that the IS used in the experiment is to support decision-making and the information provided by this system are strictly quantitative, which may have caused some conflict in the analysis of the criteria involved in the decision-making process. This is because the criteria of teaching, research, extension and management are interconnected such that one reflects on another. Thus, the opinion of the managers does not depend exclusively on quantitative data, but also of knowledge and value judgment that each manager has about the problem to be solved.

Reasoning over knowledge-based generation of situations in context spaces to reduce food waste

With the ever-growing amount of connected sensors (IoT), making sense of sensed data becomes even more important. Pervasive computing is a key enabler for sustainable solutions, prominent examples are smart energy systems and decision support systems. A key feature of pervasive systems is situation awareness which allows a system to thoroughly understand its environment. It is based on external interpretation of data and thus relies on expert knowledge. Due to the distinct nature of situations in different domains and applications, the development of situation aware applications remains a complex process. This thesis is concerned with a general framework for situation awareness which simplifies the development of applications. It is based on the Situation Theory Ontology to provide a foundation for situation modelling which allows knowledge reuse. Concepts of the Situation Theory are mapped to the Context Space Theory which is used for situation reasoning. Situation Spaces in the Context Space are automatically generated with the defined knowledge. For the acquisition of sensor data, the IoT standards O-MI/O-DF are integrated into the framework. These allow a peer-to-peer data exchange between data publisher and the proposed framework and thus a platform independent subscription to sensed data. The framework is then applied for a use case to reduce food waste. The use case validates the applicability of the framework and furthermore serves as a showcase for a pervasive system contributing to the sustainability goals. Leading institutions, e.g. the United Nations, stress the need for a more resource efficient society and acknowledge the capability of ICT systems. The use case scenario is based on a smart neighbourhood in which the system recommends the most efficient use of food items through situation awareness to reduce food waste at consumption stage.

Mission and Scenario Planning for Unmanned Aerial Vehicles (Path Planning and Collision Avoidance Systems)

As unmanned autonomous vehicles (UAVs) are being widely utilized in military and civil applications, concerns are growing about mission safety and how to integrate dierent phases of mission design. One important barrier to a coste ective and timely safety certication process for UAVs is the lack of a systematic approach for bridging the gap between understanding high-level commander/pilot intent and implementation of intent through low-level UAV behaviors. In this thesis we demonstrate an entire systems design process for a representative UAV mission, beginning from an operational concept and requirements and ending with a simulation framework for segments of the mission design, such as path planning and decision making in collision avoidance. In this thesis, we divided this complex system into sub-systems; path planning, collision detection and collision avoidance. We then developed software modules for each sub-system

Introducing a pilot data collection model for real-time evaluation of data redundancy

In order to reduce serious health incidents, individuals with high risks need to be identified as early as possible so that effective intervention and preventive care can be provided. This requires regular and efficient assessments of risk within communities that are the first point of contacts for individuals. Clinical Decision Support Systems CDSSs have been developed to help with the task of risk assessment, however such systems and their underpinning classification models are tailored towards those with clinical expertise. Communities where regular risk assessments are required lack such expertise. This paper presents the continuation of GRiST research team efforts to disseminate clinical expertise to communities. Based on our earlier published findings, this paper introduces the framework and skeleton for a data collection and risk classification model that evaluates data redundancy in real-time, detects the risk-informative data and guides the risk assessors towards collecting those data. By doing so, it enables non-experts within the communities to conduct reliable Mental Health risk triage.

Evaluación del desempeño pronóstico de dos puntajes de predicción de mortalidad a siete días en pacientes adultos oncológicos críticamente enfermos admitidos a una unidad de cuidados intensivos en Bogotá

Introducción Los sistemas de puntuación para predicción se han desarrollado para medir la severidad de la enfermedad y el pronóstico de los pacientes en la unidad de cuidados intensivos. Estas medidas son útiles para la toma de decisiones clínicas, la estandarización de la investigación, y la comparación de la calidad de la atención al paciente crítico. Materiales y métodos Estudio de tipo observacional analítico de cohorte en el que reviso las historias clínicas de 283 pacientes oncológicos admitidos a la unidad de cuidados intensivos (UCI) durante enero de 2014 a enero de 2016 y a quienes se les estimo la probabilidad de mortalidad con los puntajes pronósticos APACHE IV y MPM II, se realizó regresión logística con las variables predictoras con las que se derivaron cada uno de los modelos es sus estudios originales y se determinó la calibración, la discriminación y se calcularon los criterios de información Akaike AIC y Bayesiano BIC. Resultados En la evaluación de desempeño de los puntajes pronósticos APACHE IV mostro mayor capacidad de predicción (AUC = 0,95) en comparación con MPM II (AUC = 0,78), los dos modelos mostraron calibración adecuada con estadístico de Hosmer y Lemeshow para APACHE IV (p = 0,39) y para MPM II (p = 0,99). El ∆ BIC es de 2,9 que muestra evidencia positiva en contra de APACHE IV. Se reporta el estadístico AIC siendo menor para APACHE IV lo que indica que es el modelo con mejor ajuste a los datos. Conclusiones APACHE IV tiene un buen desempeño en la predicción de mortalidad de pacientes críticamente enfermos, incluyendo pacientes oncológicos. Por lo tanto se trata de una herramienta útil para el clínico en su labor diaria, al permitirle distinguir los pacientes con alta probabilidad de mortalidad.

Quality of Judgment Assessment

The intersection of Artificial Intelligence and The Law stands for a multifaceted matter, and its effects set the advances on culture, organization, as well as the social matters, when the emergent information technologies are taken into consideration. From this point of view, the weight of formal and informal Conflict Resolution settings should be highlighted, and the use of defective data, information or knowledge must be emphasized. Indeed, it is hard to do it with traditional problem solving methodologies. Therefore, in this work the focus is on the development of decision support systems, in terms of its knowledge representation and reasoning procedures, under a formal framework based on Logic Programming, complemented with an approach to computing centered on Artificial Neural Networks. It is intended to evaluate the Quality-of-Judgments and the respective Degree-of-Confidence that one has on such happenings.

A Case-base Approach to Workforces’ Satisfaction Assessment

It is well known that human resources play a valuable role in a sustainable organizational development. Indeed, this work will focus on the development of a decision support system to assess workers’ satisfaction based on factors related to human resources management practices. The framework is built on top of a Logic Programming approach to Knowledge Representation and Reasoning, complemented with a Case Based approach to computing. The proposed solution is unique in itself, once it caters for the explicit treatment of incomplete, unknown, or even self-contradictory information, either in terms of a qualitative or quantitative setting. Furthermore, clustering methods based on similarity analysis among cases were used to distinguish and aggregate collections of historical data or knowledge in order to reduce the search space, therefore enhancing the cases retrieval and the overall computational process.

An Artificial Intelligence Approach to Individuals' Age Assessment

A link between patterns of pelvic growth and human life history is supported by the finding that, cross-culturally, variation in maturation rates of female pelvis are correlated with variation in ages of menarche and first reproduction, i.e., it is well known that the human dimensions of the pelvic bones depend on the gender and vary with the age. Indeed, one feature in which humans appear to be unique is the prolonged growth of the pelvis after the age of sexual maturity. Both the total superoinferior length and mediolateral breadth of the pelvis continues to grow markedly after puberty, and do not reach adult proportions until the late teens years. This continuation of growth is accomplished by relatively late fusion of the separate centers of ossification that form the bones of the pelvis. Hence, in this work we will focus on the development of an intelligent decision support system to predict individual’s age based on a pelvis' dimensions criteria. Some basic image processing techniques were applied in order to extract the relevant features from pelvic X-rays, being the computational framework built on top of a Logic Programming approach to Knowledge Representation and Reasoning that caters for the handling of incomplete, unknown, or even self-contradictory information, complemented with a Case Base approach to computing.

Governing algorithms in the big data era for balancing new digital rights - designing GDPR compliant and trustworthy XAI systems

This thesis investigates the legal, ethical, technical, and psychological issues of general data processing and artificial intelligence practices and the explainability of AI systems. It consists of two main parts. In the initial section, we provide a comprehensive overview of the big data processing ecosystem and the main challenges we face today. We then evaluate the GDPR’s data privacy framework in the European Union. The Trustworthy AI Framework proposed by the EU’s High-Level Expert Group on AI (AI HLEG) is examined in detail. The ethical principles for the foundation and realization of Trustworthy AI are analyzed along with the assessment list prepared by the AI HLEG. Then, we list the main big data challenges the European researchers and institutions identified and provide a literature review on the technical and organizational measures to address these challenges. A quantitative analysis is conducted on the identified big data challenges and the measures to address them, which leads to practical recommendations for better data processing and AI practices in the EU. In the subsequent part, we concentrate on the explainability of AI systems. We clarify the terminology and list the goals aimed at the explainability of AI systems. We identify the reasons for the explainability-accuracy trade-off and how we can address it. We conduct a comparative cognitive analysis between human reasoning and machine-generated explanations with the aim of understanding how explainable AI can contribute to human reasoning. We then focus on the technical and legal responses to remedy the explainability problem. In this part, GDPR’s right to explanation framework and safeguards are analyzed in-depth with their contribution to the realization of Trustworthy AI. Then, we analyze the explanation techniques applicable at different stages of machine learning and propose several recommendations in chronological order to develop GDPR-compliant and Trustworthy XAI systems.

Comparative study of chemical and mechanical retentive systems for bonding of indirect composite resin to commercially pure titanium

This study evaluated the effect of chemical and mechanical surface treatments for cast metal alloys on the bond strength of an indirect composite resin (Artglass) to commercially pure titanium (cpTi). Thirty cylindrical metal rods (3 mm diameter x 60 mm long) were cast in grade-1 cpTi and randomly assigned to 6 groups (n=5) according to the received surface treatment: sandblasting; chemical treatment; mechanical treatment - 0.4 mm beads; mechanical treatment - 0.6 mm beads; chemical/mechanical treatment - 0.4 mm; and chemical/mechanical treatment - 0.6 mm beads. Artglass rings (6.0 mm diameter x 2.0 mm thick) were light cured around the cpTi rods, according manufacturer's specifications. The specimens were invested in hard gypsum and their bond strength (in MPa) to the rods was measured at fracture with a universal testing machine at a crosshead speed of 2.0 mm/min and 500 kgf load cell. Data were analyzed statistically by one-way ANOVA and Tukey test (a=5%). The surface treatments differed significantly from each other (p<0.05) regarding the recorded bond strengths. Chemical retention and sandblasting showed statistically similar results to each other (p=0.139) and both had significantly lower bond strengths (p<0.05) than the other treatments. In conclusion, mechanical retention, either associated or not to chemical treatment, provided higher bond strength of the indirect composite resin to cpTi.

Integration and visualization of systems biology data in context of the genome

Background: High-density tiling arrays and new sequencing technologies are generating rapidly increasing volumes of transcriptome and protein-DNA interaction data. Visualization and exploration of this data is critical to understanding the regulatory logic encoded in the genome by which the cell dynamically affects its physiology and interacts with its environment. Results: The Gaggle Genome Browser is a cross-platform desktop program for interactively visualizing high-throughput data in the context of the genome. Important features include dynamic panning and zooming, keyword search and open interoperability through the Gaggle framework. Users may bookmark locations on the genome with descriptive annotations and share these bookmarks with other users. The program handles large sets of user-generated data using an in-process database and leverages the facilities of SQL and the R environment for importing and manipulating data. A key aspect of the Gaggle Genome Browser is interoperability. By connecting to the Gaggle framework, the genome browser joins a suite of interconnected bioinformatics tools for analysis and visualization with connectivity to major public repositories of sequences, interactions and pathways. To this flexible environment for exploring and combining data, the Gaggle Genome Browser adds the ability to visualize diverse types of data in relation to its coordinates on the genome. Conclusions: Genomic coordinates function as a common key by which disparate biological data types can be related to one another. In the Gaggle Genome Browser, heterogeneous data are joined by their location on the genome to create information-rich visualizations yielding insight into genome organization, transcription and its regulation and, ultimately, a better understanding of the mechanisms that enable the cell to dynamically respond to its environment.